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Table of Contents

Metron Benefits. SOC Analyst & Investigator Perspective

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The above diagram illustrates the key steps in a typical analyst/investigator workflow. For certain steps in this workflow, Apache Metron provides keys capabilities not found in traditional security tools:

Looking through Alerts

Centralized Alerts Console - Having a centralized dashboard for alerts and the telemetry events associated with the alert across all security data sources in your enterprise is a powerful feature within Metron that prevents the Analyst from jumping from one console to another.

Meta Alerts - The long term vision of Metron is to provide a suite of analytical models and packs including Alerts Relevancy Engine and Meta-Alerts. Meta Alerts are generated by groupings or analytics models and provide a mechanism to shield the end user from being inundated with 1000s of granular alerts.

Alerts labeled with threat intel data - Viewing alerts labeled with threat intel from third party feeds allows the analyst to decipher more quickly which alerts are legitimate vs false positives.

Collecting Contextual data

Fully enriched messages - Analyst spend a lot of time manually enriching the raw alerts or events. With Metron, analysts work with the fully enriched message.

Single Pane of Glass UI - Single pane of glass that not only has all alerts across different security data sources but also the same view that provides the enriched data

Centralized real-time search - All alerts and telemetry events are indexed in real-time. Hence, the analyst has immediate access to search for all events.

All logs in one place - All events with the enrichments and labels are stored in a single repository.

Investigate

Granular access to PCAP - After identifying a legitimate threat, more advanced SOC investigators want the ability to download the raw packet data that caused the alert. Metron provides this capability.

Replay old PCAP against new signatures - Metron can be configured to store raw pcap data in Hadoop for a configurable period of time. This corpus of pcap data can then be replayed to test new analytical models and new signatures.

Tag Behavior for modeling by data scientists

Raw messages used as evidentiary store

Asset inventory and User Identity as enrichment sources.

Note that the above 3 steps in the analyst workflow make up approximately 70% of the time. Metron will drastically decrease the analyst workflow time spend because everything the SOC analyst needs to know is in a single place.

Metron Benefits? Data Scientist Perspective

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The above diagram illustrates the key steps in a typical data science workflow. For certain steps in this workflow, Apache Metron provides key capabilities not found in traditional security tools:

Finding the data

All my data is in the same place - One of the biggest challenges faced by security data scientists is to find the data required to train and evaluate the score models. Metron provides a single repository where the enterprise’s security telemetry data are stored.

Data exposed through a variety of APIs - The Metron security vault/repository provides different engines to access and work with the data including SQL, scripting languages, in-memory, java, scala, key-value columnar, REST APIs, User Portals, etc..

Metron normalizes telemetry events - As discussed in the first blog where we traced an event being processed by the platform, Metron normalizes all telemetry data into at least a standard 7 tuple json structure allowing data scientists to find and correlate data together more easily.

Partial schema validation on ingest - Metron framework will validate data on ingest and will filter out bad data automatically which is something that data scientists, traditionally, spend a lot time doing.

Munging Data

Automatic data enrichment - Typically data scientists have to manually enrich data to create and test features or have to work with the data/platform team to do so. With Metron, events are enriched in real-time as it comes in and the enriched event is stored in the Metron security vault.

Automatic application of class labels - Different types of metadata (threat intel information, etc…) is tagged on to the event which allows the data scientists to create feature matrixes for models more easily.

Massively parallel computation framework - All the cleaning and munging of the data is using distributed technologies that allows the processing of these high velocity/ large volumes to be performant and scalable.

Apache Zeppelin Dashboards - Out of the box Zeppelin dashboards will be available that can be used by SOC analysts. With Zeppelin you can share the dashboards, substitute variables, and can quickly change graph types. An example of a dashboard would be to show all HTTP calls that resulted in 404 errors, visualized as a bar graph ordered by the number of failures.

Integration with Jupyter - Jupyter notebooks will be provided to data scientists for common tasks such as exploration, visualization, plotting, evaluating features, etc..

Note that the above 4 steps in the data science workflow make up approximately 80% of the time. Metron will drastically reduce the time from hypothesis to model for the data scientist.